2018 - IEEE Fellow For contributions to apprenticeship and reinforcement learning for robotics and autonomous systems
2011 - Fellow of Alfred P. Sloan Foundation
2011 - Hellman Fellow
His primary areas of study are Artificial intelligence, Reinforcement learning, Robot, Artificial neural network and Machine learning. The Artificial intelligence study combines topics in areas such as Markov decision process and Computer vision. The concepts of his Reinforcement learning study are interwoven with issues in Principle of maximum entropy, Mathematical optimization, Benchmark, Function and Key.
When carried out as part of a general Robot research project, his work on Motion planning is frequently linked to work in Task analysis, therefore connecting diverse disciplines of study. His work carried out in the field of Artificial neural network brings together such families of science as Feature engineering, Control, Training set and Set. His study in the fields of Supervised learning under the domain of Machine learning overlaps with other disciplines such as Meta learning.
Artificial intelligence, Reinforcement learning, Robot, Machine learning and Artificial neural network are his primary areas of study. His research ties Computer vision and Artificial intelligence together. His Reinforcement learning study incorporates themes from Human–computer interaction, Control, Mathematical optimization, Set and Sample.
He combines subjects such as Object, Control engineering, Trajectory and Domain with his study of Robot. His Machine learning research incorporates themes from Generalization and Adaptation. A large part of his Artificial neural network studies is devoted to Supervised learning.
Pieter Abbeel spends much of his time researching Reinforcement learning, Artificial intelligence, Machine learning, Human–computer interaction and Code. The study incorporates disciplines such as SIGNAL, Control, Set, Robot and Sample in addition to Reinforcement learning. His Robot study combines topics from a wide range of disciplines, such as Object, Field and Navigation system.
His Artificial intelligence research incorporates elements of Generalization and State. His studies deal with areas such as Encoder and Representation as well as Machine learning. His Human–computer interaction study combines topics in areas such as Teleoperation and Plan.
Pieter Abbeel mainly focuses on Artificial intelligence, Reinforcement learning, Machine learning, Code and Generalization. The various areas that Pieter Abbeel examines in his Artificial intelligence study include Stability and Pattern recognition. His study in Reinforcement learning is interdisciplinary in nature, drawing from both Domain, Control, Feature learning, Adaptation and Sample.
Pieter Abbeel has researched Domain in several fields, including SIGNAL, Human–computer interaction, Object, Robot and Key. His specific area of interest is Machine learning, where Pieter Abbeel studies Artificial neural network. His biological study deals with issues like Generative grammar, which deal with fields such as Contextual image classification.
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Trust Region Policy Optimization
John Schulman;Sergey Levine;Pieter Abbeel;Michael Jordan.
international conference on machine learning (2015)
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel;Andrew Y. Ng.
international conference on machine learning (2004)
Trust Region Policy Optimization
John Schulman;Sergey Levine;Philipp Moritz;Michael I. Jordan.
arXiv: Learning (2015)
End-to-end training of deep visuomotor policies
Sergey Levine;Chelsea Finn;Trevor Darrell;Pieter Abbeel.
Journal of Machine Learning Research (2016)
InfoGAN: interpretable representation learning by information maximizing generative adversarial nets
Xi Chen;Yan Duan;Rein Houthooft;John Schulman.
neural information processing systems (2016)
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn;Pieter Abbeel;Sergey Levine.
international conference on machine learning (2017)
Discriminative probabilistic models for relational data
Ben Taskar;Pieter Abbeel;Daphne Koller.
uncertainty in artificial intelligence (2002)
An Application of Reinforcement Learning to Aerobatic Helicopter Flight
Pieter Abbeel;Adam Coates;Morgan Quigley;Andrew Y. Ng.
neural information processing systems (2006)
A Survey of Research on Cloud Robotics and Automation
Ben Kehoe;Sachin Patil;Pieter Abbeel;Ken Goldberg.
IEEE Transactions on Automation Science and Engineering (2015)
High-Dimensional Continuous Control Using Generalized Advantage Estimation
John Schulman;Philipp Moritz;Sergey Levine;Michael Jordan.
arXiv: Learning (2015)
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